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The data and Information Literacy of runners: quantifying diet and activity

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Presentation for the European Conference on Information Literacy, 24-27th September 2018, Oulu Finland. Reports on a quantitative study that investigated the health, diet and fitness tracking behaviours of members of the Parkrun organisation in the UK

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The data and Information Literacy of runners: quantifying diet and activity

  1. 1. The Data and Information Literacy of runners: Quantifying diet and activity Pamela McKinney p.mckinney@sheffield.ac.uk Andrew Cox a.m.cox@sheffield.ac.uk Laura Sbaffi l.sbaffi@sheffield.ac.uk
  2. 2. Presentation structure • Background: quantified self and health and fitness tracking • Study methodology • Demographic data • Tracking practices • Themes relating to Information Literacy • Conclusions
  3. 3. Background: The quantified self • Self-tracking is defined as “practices in which people knowingly and purposively collect information about themselves, which they then review and consider applying to the conduct of their lives” (Lupton, 2016). • Smartphones are widely used, these highly connected devices facilitate health and fitness tracking. • Mintel estimate that 38% of UK consumers have an interest in wearable technology to monitor health and fitness (Mintel, 2017)
  4. 4. Background: the popularity of mobile apps for health and wellbeing • The popular app MyFitnessPal has 165 million users worldwide • There are over 10,000 apps that support diet monitoring or weight loss (Azar, 2013) • Use of apps can motivate people to adopt healthy behaviours, including a healthy diet, increased physical activity and weight loss (Ernsting et al., 2017; Wang et al., 2016) • Tracking can give people a sense that they are taking control of aspects of their life (Lupton 2016)
  5. 5. Background: mobile apps and people who run • People who run tend to interweave various activity trackers, sometimes with seemingly the same functionality (Rooksby et al., 2014) • Tracking is often social and collaborative rather than personal while, at the same time, that there are different styles of tracking, including goal driven and documentary tracking (Rooksby et al., 2014) • Users tend not to use apps regularly, but do frequently return to them, suggesting that there are times when applications are valuable to their users (Lin, Althoff and Leskovec 2018)
  6. 6. Information literacy in food and activity tracking: previous research 1. Understanding the importance of quality in data inputs; 2. Ability to interpret tracking information outputs in the context of the limitations of the technology; 3. Awareness of data privacy and ownership; 4. Appropriate management of information sharing.(Cox, McKinney, & Goodale, 2017)
  7. 7. Aims & Objectives • What health and fitness tracking do people in the community do, and why? • What barriers to effective and safe use do they encounter, particularly those relating to IL? • 3 study populations, Parkrun, Diabetes.co.uk, IBS Network
  8. 8. Parkrun • Founded in the UK in 2004, Parkrun is a not-for-profit organisation that organises weekly timed 5K runs in public spaces • Events are free to enter and organised by volunteers, and Parkrun’s ethos emphasises inclusivity
  9. 9. Methodology • 12 questions including: a. demographic questions b. questions that focused on use of diet and/or fitness apps and other technologies c. reasons for logging • Advertised online through parkrun UK (http://www.parkrun.org.uk/) • 143 complete responses (although 414 records were received in total) • Qualitative data collected with the question “Is there anything else you’d like to tell us about your logging practice?”
  10. 10. Sample demographics 10 Male, 31% Female, 68% Other, 1% Gender 18-24 years, 9.1% 25-34 years, 13.3% 35-44 years, 34.3% 45-54 years, 30.8% 55-64 years, 9.1% 65+ years, 3.5% Age GCSE, 7.7% "A" level, 16.8% Undergradu ate, 43.4% Postgraduat e, 30.1% Prefer not to say, 2.1% Education
  11. 11. How long have you been running for? 11 0 5 10 15 20 25 30 35 40 LESS THAN 2 YEARS 2-5 YEARS 6-10 YEARS MORE THAN 10 YEARS 37.8 35.7 14.0 12.6 %
  12. 12. How often do you run? McKinney, Sbaffi, Cox - Information School, University of Sheffield 12 0 5 10 15 20 25 30 35 1 DAY/WEEK 2 DAYS/WEEK 3 DAYS/WEEK 4 DAYS/WEEK 5 DAYS/WEEK 6 DAYS WEEK 7 DAYS/WEEK 7.7 21.7 35.0 22.4 7.7 4.9 0.7 %
  13. 13. Logging practice • Parkrunners were heavy users of devices that record running (35.7% using one every day, and 55.2% using one 2-3 times a week) • On average respondents used at least 2 apps, some as many as 4 or 5 • Recording of heart rate or other vital signs was popular, with 32.9% doing this daily and 18.9% doing it 2-3 times a week • Step counters were used every day by 57.8% of respondents • 31.5% used a food logging app every day (but find diet tracking monotonous and boring) • Mood tracking and tracking specific aspects of the diet were not popular • Strava (64.3%) and MyFitnessPal (45.5%)were the most popular apps
  14. 14. Motivations for tracking McKinney, Sbaffi, Cox - Information School, University of Sheffield 14 0 10 20 30 40 50 60 70 80 I like to try out the latest gadgets I am interested in understanding how my body works I want to manage my weight I want to improve my physical performance I want to manage a medical condition I want to identify causes of symptoms of a medical condition Other reasons 18.2 35.0 54.5 77.6 5.6 3.5 25.2 %
  15. 15. Motivations for tracking “Logging my runs has help me improve my speed and endurance which I do not think I would have done without it” “Logging and tracking has contributed to a 24kg weight reduction in 12 months” “I logged and referred to my steps daily as part of two challenges. One to do 10000 steps a day for one week for WI and another was to do 12,000 on average a day for the whole of Lent.”
  16. 16. Information literacy: Data quality • 81.1% agreed that they are careful about data entry • 79.7% agreed that they use apps to track long term trends in their activity • If food logging, 65.1% agreed they log absolutely everything they eat • 52.2% of food loggers had concerns about the quality of data entered by other people in the app. • Many reported issues with data quality and accuracy in food logging apps “Many apps are US based which means it's sometimes hard to find UK foods, but most of the time the barcode scanning works. Where it's less accurate is things like cherry tomatoes. I don't weigh them every time but I know an average weight that I use so I can go by quantity.”
  17. 17. Information Literacy: interpreting tracked information • Highly confident: 84.6% agreed they could understand the charts produced • Qualitative responses revealed a nuanced understanding of tracked data and relationship with health and wellbeing “I initially used My fitness pal to see how many calories were in specific foods and also to see how the calories balanced against manually inputted exercise. Then I got a Fitbit and linked the 2. I am type 1 diabetic and am interested in keeping my weight at a healthy BMI. I also use Endomondo for logging runs and the training plan in it for my first half marathon in September”
  18. 18. Information literacy: Data privacy • Only 28% are concerned about how the app provider might re use their data • Some recognition in the qualitative data that geolocation data made public in apps is a potential concern “I stopped using strava because you could not hide runs from the public, which is a privacy concern as they could see or workout where I live and where I run on a regular basis.”
  19. 19. Information literacy: Data sharing Who do they share data with? McKinney, Sbaffi, Cox - Information School, University of Sheffield 19 44.1 43.4 55.9 41.3 2.8 7.0 6.3 %
  20. 20. Views on sharing data • Only 29.4% agree they feel uncomfortable sharing data with their friends “Seeing what my friends are doing (and knowing that they see what I do) is a major motivator for me in exercise and encourages me to get out and do things when I don't necessary feel like it. I also like statistics and tracking my performance.” “The social features of the app I use (Strava) are also a motivating and fun feature and I love being able to look at maps of where my friends have run to give me ideas for new routes to try out myself.”
  21. 21. Conclusions • Food logging is boring and fraught with issues of data quality, it requires people to become knowledgeable about nutrition • Activity tracking is easy and enjoyable, data collection is automatic so therefore it is of high quality • Use of multiple devices and tracking different kinds of data leads to enhanced understanding of the body, and awareness of using data to support health and wellbeing • Information literacy in terms of tracked data is high: people understand their data • Sharing activity data with friends is a motivating factor, it is part of the enjoyment of the activity • People lack awareness of the potential for their data to be sold and re-used
  22. 22. References Azar, K. M. J., Lesser, L. I., Laing, B. Y., Stephens, J., Aurora, M. S., Burke, L. E., & Palaniappan, L. P. (2013). Mobile applications for weight management: theory-based content analysis. American Journal of Preventive Medicine, 45(5), 583–589. https://doi.org/10.1016/j.amepre.2013.07.005 Cox, A. M., Mckinney, P. A., & Goodale, P. (2017). Food logging: an Information Literacy perspective. Aslib Journal of Information Management, 69(2). https://doi.org/10.1108/09574090910954864 Ernsting, C., Dombrowski, S. U., Oedekoven, M., O’Sullivan, J. L., Kanzler, E., Kuhlmey, A., & Gellert, P. (2017). Using smartphones and health apps to change and manage health behaviors: A population-based survey. Journal of Medical Internet Research, 19(4), 1–12. https://doi.org/10.2196/jmir.6838 Lin, Z., Althoff, T., & Leskovec, J. (2018). I’ll Be Back: On the Multiple Lives of Users of a Mobile Activity Tracking Application. In Proceedings of the International World Wide Web Conference. April 2018 (pp. 1501–1511). https://doi.org/10.1145/3178876.3186062 Lupton, D. (2016). The quantified self. Cambridge: Polity Press. Mintel. (2017). Wearable technology - UK - December 2017. Rooksby, J., Rost, M., Morrison, A., & Chalmers, M. C. (2014). Personal tracking as lived informatics. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems - CHI ’14, 1163–1172. https://doi.org/10.1145/2556288.2557039 Wang, Q., Egelandsdal, B., Amdam, G. V, Almli, V. L., & Oostindjer, M. (2016). Diet and Physical Activity Apps: Perceived Effectiveness by App Users. JMIR MHealth and UHealth, 4(2), e33. https://doi.org/10.2196/mhealth.5114

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